Final Report

We were excited to do our report over this data because it was relatively tidy and had quite a few categorical variables and options for additional columns to graph.

Introduction

• Air pollution can be detrimental to both our health and the climate
  • Outdoor and indoor air pollution cause chronic pain, respiratory diseases, shortened lifespan
  • Air pollution kills about 7 million people worldwide every year
  • Hopefully this information can showcase the importance of air pollution and that we should be more mindful about our planet
• Overview
  • We will see how different air pollution types affect the population
  • compare past and present population numbers
  • determine which air pollutant type has the highest associated death rate

Packages Required

#This will allow us to filter through our data 
library(tidyverse)
library(dplyr)
#This will help us plot figures to showcase our findings
library(ggplot2)
#This will help us organize and display our data as necessary 
library(knitr)
library(kableExtra)
#This expands our plot uses 
library(plotly)
#Scientific Notation Disabled 
options(scipen=T)

Deaths Data

• Deaths from pollution:
  • Kaggle: [https://www.kaggle.com/datasets/akshat0giri/death-due-to-air-pollution-19902017]
  • Author: Akshat Giri
  • Last update: 2 years ago

Import the deaths-due-to-air-pollution data

deaths_df_old <- data.frame(read.csv("death-rates-from-air-pollution.csv"))
glimpse(deaths_df_old)

## Rows: 6,468
## Columns: 7
## $ Entity                                          <chr> "Afghanistan", "Afghan…
## $ Code                                            <chr> "AFG", "AFG", "AFG", "…
## $ Year                                            <int> 1990, 1991, 1992, 1993…
## $ Air.pollution..total...deaths.per.100.000.      <dbl> 299.4773, 291.2780, 27…
## $ Indoor.air.pollution..deaths.per.100.000.       <dbl> 250.3629, 242.5751, 23…
## $ Outdoor.particulate.matter..deaths.per.100.000. <dbl> 46.44659, 46.03384, 44…
## $ Outdoor.ozone.pollution..deaths.per.100.000.    <dbl> 5.616442, 5.603960, 5.…

Fixed: use rename instead of colnames

We are going to rename a few of the columns and glimpse the data

deaths_df<- deaths_df_old %>% rename(country=Entity, acronym=Code, year=Year, total_deaths=Air.pollution..total...deaths.per.100.000., indoor_deaths=Indoor.air.pollution..deaths.per.100.000., outdoor_deaths=Outdoor.particulate.matter..deaths.per.100.000., ozone_deaths=Outdoor.ozone.pollution..deaths.per.100.000.)

glimpse(deaths_df)

## Rows: 6,468
## Columns: 7
## $ country        <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanist…
## $ acronym        <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG",…
## $ year           <int> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1…
## $ total_deaths   <dbl> 299.4773, 291.2780, 278.9631, 278.7908, 287.1629, 288.0…
## $ indoor_deaths  <dbl> 250.3629, 242.5751, 232.0439, 231.6481, 238.8372, 239.9…
## $ outdoor_deaths <dbl> 46.44659, 46.03384, 44.24377, 44.44015, 45.59433, 45.36…
## $ ozone_deaths   <dbl> 5.616442, 5.603960, 5.611822, 5.655266, 5.718922, 5.739…

Data Variables

Variables that interest us here include:

• country
• total_deaths: per 100,000
• indoor_deaths: Indoor air pollution is considered pollution that occurs in the household. Cooking with solid fuels:
  • Wood
  • Crop waste, dung
  • Charcoal, coal
• outdoor_deaths: Outdoor air or ambient air are emissions caused by combustion processes from motor vehicles, solid fuel burning and industry
  • Ozone (O3)
  • Particulate matter (PM10 and PM2.5)
  • Nitrogen dioxide (NO2)
  • Carbon monoxide (CO)
  • Sulfur dioxide (SO2)
• ozone_deaths: Ozone is a gas that occurs both in Earth’s upper atmosphere and at ground level. Ozone in the atmosphere is an important and helpful greenhouse gas, but ground-level ozone is created by extensive use of fossil fuels:
  • Pollutants emitted by cars
  • Power plants, industrial boilers, refineries, chemical plants

World Population Data

• World Population:
  • Kaggle: [https://www.kaggle.com/datasets/imdevskp/world-population-19602018]
  • Author: Devakumar K. P.
  • Last update: 2 Years ago

Now, let’s take a look at the population data.

world_pop <- read.csv("population_total_long.csv")
glimpse(world_pop)

## Rows: 12,595
## Columns: 3
## $ Country.Name <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorra", "…
## $ Year         <int> 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 196…
## $ Count        <int> 54211, 8996973, 5454933, 1608800, 13411, 92418, 20481779,…

---

To get a general idea of ‘deaths-dataframe’ we made, let’s make a plots to see what’s happening. This is a plot of indoor x outdoor deaths around the world by country.

This is a mess, and so we chose two countries from each continent (a high-population and a low-population country) to graph.

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We selected a high population from each continent and used the formula below to determine the low population.

Low population = high population * .10

• We wanted to have a consistent variation between our selection of low and high populated countries.
• Based on our calculation we selected the low populated country that was closest to 10% of the high populated country.
• EX: U.S (high populated) = 331002651. If we multiply by .10 we get 33100265.1 and Canada (low populated) was a population of 37742154 which was closest to 10% of the US population in 2020

Country.Name Year Count Australia 1996 18311000 Brazil 1996 164614688 Germany 1996 81914831 Nigeria 1996 110668794 Pakistan 1996 127349290 United States 1996 269394000

Country.Name Year Count Canada 1996 29610218 Chile 1996 14587370 Sri Lanka 1996 18367288 Malawi 1996 10022789 New Zealand 1996 3732000 Serbia 1996 7617794

Combine Data Sets

First let’s look at a table of the high and low populated countries using the world population data set.

Country.Name Year Count Australia 1996 18311000 Brazil 1996 164614688 Germany 1996 81914831 Nigeria 1996 110668794 Pakistan 1996 127349290 United States 1996 269394000

Country.Name Year Count Canada 1996 29610218 Chile 1996 14587370 Sri Lanka 1996 18367288 Malawi 1996 10022789 New Zealand 1996 3732000 Serbia 1996 7617794

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Next, we are going to see the death count for high and low populated countries using the deaths dataframe.

country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths Australia AUS 1996 23.04465 0.3585034 22.407071 0.3249375 Australia AUS 1997 22.43025 0.3222224 21.838737 0.3141838 Australia AUS 1998 21.50529 0.2839769 20.960276 0.3048918 Australia AUS 1999 20.40911 0.2590092 19.897091 0.2953354 Australia AUS 2000 19.39822 0.2398763 18.909240 0.2899216 Australia AUS 2001 18.58572 0.2234341 18.118700 0.2836469 Australia AUS 2002 18.11849 0.2105980 17.662269 0.2859938 Australia AUS 2003 17.23830 0.1937083 16.802536 0.2816949 Australia AUS 2004 16.34770 0.1760229 15.932077 0.2785466 Australia AUS 2005 15.41337 0.1599279 15.016089 0.2757150 Australia AUS 2006 14.92239 0.1496469 14.530223 0.2819060 Australia AUS 2007 14.92140 0.1449723 14.514884 0.3042005 Australia AUS 2008 14.64683 0.1383225 14.228709 0.3254648 Australia AUS 2009 14.11563 0.1259313 13.694572 0.3431982 Australia AUS 2010 13.57171 0.1174834 13.140380 0.3647233 Australia AUS 2011 13.72763 0.1119247 13.276676 0.3956796 Australia AUS 2012 12.65973 0.1018626 12.196401 0.4192914 Australia AUS 2013 11.87449 0.0973836 11.384154 0.4530427 Australia AUS 2014 11.47268 0.0931036 10.939491 0.5037056 Australia AUS 2015 11.27679 0.0886376 10.702072 0.5544068 Australia AUS 2016 10.58644 0.0844017 9.974549 0.5955779 Australia AUS 2017 10.79595 0.0833628 10.128111 0.6592419

country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths Canada CAN 1996 22.18101 0.0946226 20.155243 2.192488 Canada CAN 1997 21.92768 0.0877542 19.908473 2.195940 Canada CAN 1998 21.65538 0.0824492 19.634839 2.205681 Canada CAN 1999 21.17703 0.0751278 19.179045 2.189426 Canada CAN 2000 20.26486 0.0681836 18.326999 2.127733 Canada CAN 2001 19.82451 0.0641108 17.938427 2.076464 Canada CAN 2002 19.52428 0.0604824 17.669133 2.047603 Canada CAN 2003 19.17033 0.0564743 17.338627 2.026864 Canada CAN 2004 18.40919 0.0513588 16.629516 1.973025 Canada CAN 2005 17.79268 0.0481667 16.030102 1.954712 Canada CAN 2006 17.14391 0.0447622 15.445519 1.888735 Canada CAN 2007 16.93196 0.0435468 15.229981 1.895259 Canada CAN 2008 16.51814 0.0407468 14.829238 1.883242 Canada CAN 2009 15.76760 0.0380831 14.118647 1.838920 Canada CAN 2010 14.88338 0.0340653 13.281852 1.786430 Canada CAN 2011 14.59934 0.0319160 13.030477 1.756998 Canada CAN 2012 13.82968 0.0307105 12.243601 1.764727 Canada CAN 2013 12.97501 0.0288027 11.410021 1.733997 Canada CAN 2014 12.61872 0.0276959 11.032571 1.746991 Canada CAN 2015 12.21793 0.0270578 10.609097 1.763895 Canada CAN 2016 11.00267 0.0251286 9.397502 1.740834 Canada CAN 2017 10.71662 0.0247705 9.110733 1.739718

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Lastly, we will join the population and and deaths with its respected country.

country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths Count Australia AUS 1996 23.04465 0.3585034 22.407071 0.3249375 18311000 Australia AUS 1997 22.43025 0.3222224 21.838737 0.3141838 18517000 Australia AUS 1998 21.50529 0.2839769 20.960276 0.3048918 18711000 Australia AUS 1999 20.40911 0.2590092 19.897091 0.2953354 18926000 Australia AUS 2000 19.39822 0.2398763 18.909240 0.2899216 19153000 Australia AUS 2001 18.58572 0.2234341 18.118700 0.2836469 19413000 Australia AUS 2002 18.11849 0.2105980 17.662269 0.2859938 19651400 Australia AUS 2003 17.23830 0.1937083 16.802536 0.2816949 19895400 Australia AUS 2004 16.34770 0.1760229 15.932077 0.2785466 20127400 Australia AUS 2005 15.41337 0.1599279 15.016089 0.2757150 20394800 Australia AUS 2006 14.92239 0.1496469 14.530223 0.2819060 20697900 Australia AUS 2007 14.92140 0.1449723 14.514884 0.3042005 20827600 Australia AUS 2008 14.64683 0.1383225 14.228709 0.3254648 21249200 Australia AUS 2009 14.11563 0.1259313 13.694572 0.3431982 21691700 Australia AUS 2010 13.57171 0.1174834 13.140380 0.3647233 22031750 Australia AUS 2011 13.72763 0.1119247 13.276676 0.3956796 22340024 Australia AUS 2012 12.65973 0.1018626 12.196401 0.4192914 22733465 Australia AUS 2013 11.87449 0.0973836 11.384154 0.4530427 23128129 Australia AUS 2014 11.47268 0.0931036 10.939491 0.5037056 23475686 Australia AUS 2015 11.27679 0.0886376 10.702072 0.5544068 23815995 Australia AUS 2016 10.58644 0.0844017 9.974549 0.5955779 24190907 Australia AUS 2017 10.79595 0.0833628 10.128111 0.6592419 24601860

country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths Count Canada CAN 1996 22.18101 0.0946226 20.155243 2.192488 29610218 Canada CAN 1997 21.92768 0.0877542 19.908473 2.195940 29905948 Canada CAN 1998 21.65538 0.0824492 19.634839 2.205681 30155173 Canada CAN 1999 21.17703 0.0751278 19.179045 2.189426 30401286 Canada CAN 2000 20.26486 0.0681836 18.326999 2.127733 30685730 Canada CAN 2001 19.82451 0.0641108 17.938427 2.076464 31020902 Canada CAN 2002 19.52428 0.0604824 17.669133 2.047603 31360079 Canada CAN 2003 19.17033 0.0564743 17.338627 2.026864 31644028 Canada CAN 2004 18.40919 0.0513588 16.629516 1.973025 31940655 Canada CAN 2005 17.79268 0.0481667 16.030102 1.954712 32243753 Canada CAN 2006 17.14391 0.0447622 15.445519 1.888735 32571174 Canada CAN 2007 16.93196 0.0435468 15.229981 1.895259 32889025 Canada CAN 2008 16.51814 0.0407468 14.829238 1.883242 33247118 Canada CAN 2009 15.76760 0.0380831 14.118647 1.838920 33628895 Canada CAN 2010 14.88338 0.0340653 13.281852 1.786430 34004889 Canada CAN 2011 14.59934 0.0319160 13.030477 1.756998 34339328 Canada CAN 2012 13.82968 0.0307105 12.243601 1.764727 34714222 Canada CAN 2013 12.97501 0.0288027 11.410021 1.733997 35082954 Canada CAN 2014 12.61872 0.0276959 11.032571 1.746991 35437435 Canada CAN 2015 12.21793 0.0270578 10.609097 1.763895 35702908 Canada CAN 2016 11.00267 0.0251286 9.397502 1.740834 36109487 Canada CAN 2017 10.71662 0.0247705 9.110733 1.739718 36540268

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Combine the data based on continent.

joined_all <- right_join(deaths_df, world_pop, by=c('country' = 'Country.Name', 'year' = 'Year'))
head(joined_all)

##       country acronym year total_deaths indoor_deaths outdoor_deaths
## 1 Afghanistan     AFG 1990     299.4773      250.3629       46.44659
## 2 Afghanistan     AFG 1991     291.2780      242.5751       46.03384
## 3 Afghanistan     AFG 1992     278.9631      232.0439       44.24377
## 4 Afghanistan     AFG 1993     278.7908      231.6481       44.44015
## 5 Afghanistan     AFG 1994     287.1629      238.8372       45.59433
## 6 Afghanistan     AFG 1995     288.0142      239.9066       45.36714
##   ozone_deaths    Count
## 1     5.616442 12412308
## 2     5.603960 13299017
## 3     5.611822 14485546
## 4     5.655266 15816603
## 5     5.718922 17075727
## 6     5.739174 18110657

north_america <- joined_all %>% filter(country %in% c("United States", "Canada"))
head(na.omit(north_america))

##   country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths
## 1  Canada     CAN 1990     23.74844     0.1461597       21.82110     2.024766
## 2  Canada     CAN 1991     23.34036     0.1347912       21.40547     2.046623
## 3  Canada     CAN 1992     23.00947     0.1247982       21.06392     2.069720
## 4  Canada     CAN 1993     23.03293     0.1191081       21.03444     2.135114
## 5  Canada     CAN 1994     22.60288     0.1107671       20.59547     2.152504
## 6  Canada     CAN 1995     22.32566     0.1015955       20.28851     2.193303
##      Count
## 1 27691138
## 2 28037420
## 3 28371264
## 4 28684764
## 5 29000663
## 6 29302311

south_america <- joined_all %>% filter(country %in% c("Brazil", "Chile"))
head(na.omit(south_america))

##   country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths
## 1  Brazil     BRA 1990     74.96820      44.08928       28.36460     3.330584
## 2  Brazil     BRA 1991     71.52505      41.12989       27.91653     3.272506
## 3  Brazil     BRA 1992     69.97594      39.07269       28.37737     3.321153
## 4  Brazil     BRA 1993     69.34644      37.34668       29.37063     3.439490
## 5  Brazil     BRA 1994     66.74580      34.60871       29.48986     3.445359
## 6  Brazil     BRA 1995     63.54859      31.67095       29.22721     3.430127
##       Count
## 1 149003223
## 2 151648011
## 3 154259380
## 4 156849078
## 5 159432716
## 6 162019896

africa <- joined_all %>% filter(country %in% c("Nigeria", "Malawi"))
head(na.omit(africa))

##   country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths
## 1  Malawi     MWI 1990     167.7156      153.3657       12.60813     3.518561
## 2  Malawi     MWI 1991     167.8769      153.3428       12.77371     3.541273
## 3  Malawi     MWI 1992     171.1963      156.2008       13.19234     3.618770
## 4  Malawi     MWI 1993     175.2565      159.9608       13.45895     3.686304
## 5  Malawi     MWI 1994     180.9753      164.9773       14.10506     3.784780
## 6  Malawi     MWI 1995     183.4036      166.9812       14.48956     3.847709
##     Count
## 1 9404500
## 2 9600355
## 3 9685973
## 4 9710331
## 5 9745690
## 6 9844415

europe <- joined_all %>% filter(country %in% c("Germany", "Serbia"))
head(na.omit(europe))

##   country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths
## 1 Germany     DEU 1990     41.91322      1.600590       38.11494     2.724651
## 2 Germany     DEU 1991     40.73815      1.472532       37.08854     2.694316
## 3 Germany     DEU 1992     38.94425      1.367432       35.45345     2.622836
## 4 Germany     DEU 1993     38.25349      1.275528       34.85003     2.623219
## 5 Germany     DEU 1994     36.85860      1.182584       33.58411     2.573705
## 6 Germany     DEU 1995     35.66449      1.109101       32.47285     2.557293
##      Count
## 1 79433029
## 2 80013896
## 3 80624598
## 4 81156363
## 5 81438348
## 6 81678051

asia <- joined_all %>% filter(country %in% c("Pakistan", "Sri Lanka"))
head(na.omit(asia))

##    country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths
## 1 Pakistan     PAK 1990     144.7155      104.4196       34.80304     10.09603
## 2 Pakistan     PAK 1991     148.0120      105.5436       36.80428     10.35961
## 3 Pakistan     PAK 1992     148.6560      105.2133       37.76577     10.35540
## 4 Pakistan     PAK 1993     149.6526      104.9854       38.95704     10.37194
## 5 Pakistan     PAK 1994     151.1992      105.3557       40.06784     10.44016
## 6 Pakistan     PAK 1995     154.9523      107.2959       41.72728     10.67907
##       Count
## 1 107647921
## 2 110778648
## 3 113911126
## 4 117086685
## 5 120362762
## 6 123776839

oceania <- joined_all %>% filter(country %in% c("Australia", "New Zealand"))
head(na.omit(oceania))

##     country acronym year total_deaths indoor_deaths outdoor_deaths ozone_deaths
## 1 Australia     AUS 1990     26.70503     0.6924006       25.72983    0.3285590
## 2 Australia     AUS 1991     25.91503     0.6172074       25.02097    0.3222915
## 3 Australia     AUS 1992     25.70745     0.5594191       24.86599    0.3286297
## 4 Australia     AUS 1993     24.63559     0.4920491       23.86602    0.3232958
## 5 Australia     AUS 1994     24.38185     0.4454673       23.65269    0.3300999
## 6 Australia     AUS 1995     23.10038     0.3895721       22.43122    0.3244735
##      Count
## 1 17065100
## 2 17284000
## 3 17495000
## 4 17667000
## 5 17855000
## 6 18072000

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This is a closer view on the population growth over time in both the high and low populated countries that we selected.

Death Count

Which country has the highest average death count?

Let’s make a table depicting the high and low populated countries and their respected death count due to pollution.

country hp_average_death Australia 17.76815 Brazil 48.42928 Germany 28.10988 Nigeria 112.30157 Pakistan 144.33463 United States 26.35827

country lp_average_death Canada 18.18542 Chile 36.51321 Malawi 147.77167 New Zealand 15.92536 Serbia 80.66558 Sri Lanka 69.60383

Let’s see how this is different from continent to continent

#Mean total deaths for each continent
deaths_north <- na.omit(north_america)  %>% 
  group_by(country) %>% 
  summarize(north_america_deaths = mean(total_deaths))


deaths_south <- na.omit(south_america)  %>% 
  group_by(country) %>% 
  summarize(south_america_deaths = mean(total_deaths))


deaths_africa <- na.omit(africa)  %>% 
  group_by(country) %>% 
  summarize(africa_deaths = mean(total_deaths))


deaths_europe <- na.omit(europe)  %>% 
  group_by(country) %>% 
  summarize(europe_deaths = mean(total_deaths))


deaths_asia <- na.omit(asia)  %>% 
  group_by(country) %>% 
  summarize(asia_deaths = mean(total_deaths))


deaths_oceania <- na.omit(oceania)  %>% 
  group_by(country) %>% 
  summarize(oceania_deaths = mean(total_deaths))


#Table to view continent deaths 
kable(deaths_north, caption = "North America Average Death Count")

North America Average Death Count

country	north_america_deaths
Canada	18.18542
United States	26.35827

kable(deaths_south, caption = "South America Average Death Count")

South America Average Death Count

country	south_america_deaths
Brazil	48.42928
Chile	36.51321

kable(deaths_africa, caption = "Africa Average Death Count")

Africa Average Death Count

country	africa_deaths
Malawi	147.7717
Nigeria	112.3016

kable(deaths_asia, caption = "Asia Average Death Count")

Asia Average Death Count

country	asia_deaths
Pakistan	144.33463
Sri Lanka	69.60383

kable(deaths_europe, caption = "Europe Average Death Count")

Europe Average Death Count

country	europe_deaths
Germany	28.10988
Serbia	80.66558

kable(deaths_oceania, caption = "Oceania Average Death Count")

Oceania Average Death Count

country	oceania_deaths
Australia	17.76815
New Zealand	15.92536

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Here’s a graph to clearly visualize the previous table

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So we’ve looked at the deaths due to pollution, but what percentage of the population was affected?

In order to get rid of the leading zeros, and clean up the y-axis, we multiplied the ‘percent_high’ and ‘percent_low’ by 100,000 since the data was per 100,000 when calculating deaths.

Country.Name average_population Australia 21085646 Brazil 188017856 Germany 81914553 Nigeria 146828087 Pakistan 166653684 United States 299036073

Country.Name average_population Canada 32874340 Chile 16466330 Malawi 13442531 New Zealand 4193041 Serbia 7358242 Sri Lanka 19758408

Pollution Types

Which type of pollution has the greatest number of deaths?

country	avg_indoor	avg_outdoor	avg_ozone
Pakistan	87.7427944	50.52063	10.440656
Nigeria	75.8755074	35.21678	2.117076
Brazil	19.4258385	26.84194	2.740342
Germany	0.7170881	25.47078	2.343892
Australia	0.2485867	17.20789	0.360452
United States	0.1656402	22.79947	3.915093

country	avg_indoor	avg_outdoor	avg_ozone
Canada	0.0651156	16.38423	1.9697041
Chile	8.6932699	27.17442	0.8504919
Malawi	132.1891749	13.81151	3.3870514
New Zealand	0.2908622	15.56872	0.0727512
Serbia	35.8762796	42.71254	2.9395671
Sri Lanka	44.5428441	24.77233	0.4304406

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Pollution Over Time

Let’s look at the previous two decades and compare the death count

has there been a change?

This is the first decade 1996-2006

country High_Deaths_96 High_Deaths_01 High_Deaths_06 Australia 23.04465 18.58572 14.92239 Brazil 60.67757 49.46436 41.46829 Germany 34.72325 28.38756 23.83654 Nigeria 136.08978 123.05129 102.26653 Pakistan 155.42988 151.25352 146.09296 United States 29.99271 28.93114 25.93369

country Low_Deaths_96 Low_Deaths_01 Low_Deaths_06 Canada 22.18101 19.82451 17.14391 Chile 46.36829 37.43188 30.99058 Malawi 183.14179 165.41702 137.54033 Serbia 93.44700 83.18333 79.04236 Sri Lanka 85.28997 72.16239 66.04455 Tonga 100.66078 95.27073 88.65608

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This is the second decade 2007-2017

country High_Deaths_07 High_Deaths_12 High_Deaths_17 Australia 14.92140 12.65973 10.79595 Brazil 40.42460 35.39069 30.32108 Germany 23.45850 20.91536 19.82826 Nigeria 98.90306 84.22324 81.22147 Pakistan 143.81724 133.93887 123.21548 United States 25.11756 21.98194 18.82515

country Low_Deaths_07 Low_Deaths_12 Low_Deaths_17 Canada 16.93196 13.82968 10.71662 Chile 30.53130 27.31475 24.29921 Malawi 132.12253 116.27470 104.93508 Serbia 76.65752 72.77354 62.57853 Sri Lanka 66.05987 59.22433 38.46264 Tonga 87.81178 79.49336 70.72940

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Let’s see if there is variation by continent. Here are some tables for the first decade (1996-2006) and second decade (2007-2017) grouped by continent.

#North America 1996-2006
north_96 <- na.omit(north_america)  %>% 
  group_by(country) %>% 
  filter(year == 1996) %>% 
  summarize(avg_deaths_96 = mean(total_deaths))

north_01 <- na.omit(north_america)  %>% 
  group_by(country) %>% 
  filter(year == 2001) %>% 
  summarize(avg_deaths_01 = mean(total_deaths))

north_06 <- na.omit(north_america)  %>% 
  group_by(country) %>% 
  filter(year == 2006) %>% 
  summarize(avg_deaths_06 = mean(total_deaths))

kable(list(north_96,north_01,north_06), caption = "North America Deaths 1996-2006")

North America Deaths 1996-2006

country avg_deaths_96 Canada 22.18101 United States 29.99271

country avg_deaths_01 Canada 19.82451 United States 28.93114

country avg_deaths_06 Canada 17.14391 United States 25.93369

# North America 2007-2017
north_07 <- na.omit(north_america)  %>% 
  group_by(country) %>% 
  filter(year == 2007) %>% 
  summarize(avg_deaths_07 = mean(total_deaths))

north_12 <- na.omit(north_america)  %>% 
  group_by(country) %>% 
  filter(year == 2012) %>% 
  summarize(avg_deaths_12 = mean(total_deaths))

north_17 <- na.omit(north_america)  %>% 
  group_by(country) %>% 
  filter(year == 2017) %>% 
  summarize(avg_deaths_17 = mean(total_deaths))

kable(list(north_07,north_12,north_17), caption = "North America Deaths 2007-2017")

North America Deaths 2007-2017

country avg_deaths_07 Canada 16.93196 United States 25.11756

country avg_deaths_12 Canada 13.82968 United States 21.98194

country avg_deaths_17 Canada 10.71662 United States 18.82515

#South America 1996-2006
south_96 <- na.omit(south_america)  %>% 
  group_by(country) %>% 
  filter(year == 1996) %>% 
  summarize(avg_deaths_96 = mean(total_deaths))

south_01 <- na.omit(south_america)  %>% 
  group_by(country) %>% 
  filter(year == 2001) %>% 
  summarize(avg_deaths_01 = mean(total_deaths))

south_06 <- na.omit(south_america)  %>% 
  group_by(country) %>% 
  filter(year == 2006) %>% 
  summarize(avg_deaths_06 = mean(total_deaths))

kable(list(south_96,south_01,south_06), caption = "South America Deaths 1996-2006")

South America Deaths 1996-2006

country avg_deaths_96 Brazil 60.67757 Chile 46.36829

country avg_deaths_01 Brazil 49.46436 Chile 37.43188

country avg_deaths_06 Brazil 41.46829 Chile 30.99058

# South America 2007-2017
south_07 <- na.omit(south_america)  %>% 
  group_by(country) %>% 
  filter(year == 2007) %>% 
  summarize(avg_deaths_07 = mean(total_deaths))

south_12 <- na.omit(south_america)  %>% 
  group_by(country) %>% 
  filter(year == 2012) %>% 
  summarize(avg_deaths_12 = mean(total_deaths))

south_17 <- na.omit(south_america)  %>% 
  group_by(country) %>% 
  filter(year == 2017) %>% 
  summarize(avg_deaths_17 = mean(total_deaths))

kable(list(south_07,south_12,south_17), caption = "South America Deaths 2007-2017")

South America Deaths 2007-2017

country avg_deaths_07 Brazil 40.4246 Chile 30.5313

country avg_deaths_12 Brazil 35.39069 Chile 27.31475

country avg_deaths_17 Brazil 30.32108 Chile 24.29921

# Africa 1996-2006
africa_96 <- na.omit(africa)  %>% 
  group_by(country) %>% 
  filter(year == 1996) %>% 
  summarize(avg_deaths_96 = mean(total_deaths))

africa_01 <- na.omit(africa)  %>% 
  group_by(country) %>% 
  filter(year == 2001) %>% 
  summarize(avg_deaths_01 = mean(total_deaths))

africa_06 <- na.omit(africa)  %>% 
  group_by(country) %>% 
  filter(year == 2006) %>% 
  summarize(avg_deaths_06 = mean(total_deaths))

kable(list(africa_96,africa_01,africa_06), caption = "Africa Deaths 1996-2006")

Africa Deaths 1996-2006

country avg_deaths_96 Malawi 183.1418 Nigeria 136.0898

country avg_deaths_01 Malawi 165.4170 Nigeria 123.0513

country avg_deaths_06 Malawi 137.5403 Nigeria 102.2665

# Africa 2007-2017
africa_07 <- na.omit(africa)  %>% 
  group_by(country) %>% 
  filter(year == 2007) %>% 
  summarize(avg_deaths_07 = mean(total_deaths))

africa_12 <- na.omit(africa)  %>% 
  group_by(country) %>% 
  filter(year == 2012) %>% 
  summarize(avg_deaths_12 = mean(total_deaths))

africa_17 <- na.omit(africa)  %>% 
  group_by(country) %>% 
  filter(year == 2017) %>% 
  summarize(avg_deaths_17 = mean(total_deaths))

kable(list(africa_07,africa_12,africa_17), caption = "Africa Deaths 2007-2017")

Africa Deaths 2007-2017

country avg_deaths_07 Malawi 132.12253 Nigeria 98.90306

country avg_deaths_12 Malawi 116.27470 Nigeria 84.22324

country avg_deaths_17 Malawi 104.93508 Nigeria 81.22147

#Europe 1996-2006
europe_96 <- na.omit(europe)  %>% 
  group_by(country) %>% 
  filter(year == 1996) %>% 
  summarize(avg_deaths_96 = mean(total_deaths))

europe_01 <- na.omit(europe)  %>% 
  group_by(country) %>% 
  filter(year == 2001) %>% 
  summarize(avg_deaths_01 = mean(total_deaths))

europe_06 <- na.omit(europe)  %>% 
  group_by(country) %>% 
  filter(year == 2006) %>% 
  summarize(avg_deaths_06 = mean(total_deaths))

kable(list(europe_96,europe_01,europe_06), caption = "Europe Deaths 1996-2006")

Europe Deaths 1996-2006

country avg_deaths_96 Germany 34.72325 Serbia 93.44700

country avg_deaths_01 Germany 28.38756 Serbia 83.18333

country avg_deaths_06 Germany 23.83654 Serbia 79.04236

#Europe 2007-2017
europe_07 <- na.omit(europe)  %>% 
  group_by(country) %>% 
  filter(year == 2007) %>% 
  summarize(avg_deaths_07 = mean(total_deaths))

europe_12 <- na.omit(europe)  %>% 
  group_by(country) %>% 
  filter(year == 2012) %>% 
  summarize(avg_deaths_12 = mean(total_deaths))

europe_17 <- na.omit(europe)  %>% 
  group_by(country) %>% 
  filter(year == 2017) %>% 
  summarize(avg_deaths_17 = mean(total_deaths))

kable(list(europe_07,europe_12,europe_17), caption = "Europe Deaths 2007-2017")

Europe Deaths 2007-2017

country avg_deaths_07 Germany 23.45850 Serbia 76.65752

country avg_deaths_12 Germany 20.91536 Serbia 72.77354

country avg_deaths_17 Germany 19.82826 Serbia 62.57853

#Asia 1996-2006
asia_96 <- na.omit(asia)  %>% 
  group_by(country) %>% 
  filter(year == 1996) %>% 
  summarize(avg_deaths_96 = mean(total_deaths))

asia_01 <- na.omit(asia)  %>% 
  group_by(country) %>% 
  filter(year == 2001) %>% 
  summarize(avg_deaths_01 = mean(total_deaths))

asia_06 <- na.omit(asia)  %>% 
  group_by(country) %>% 
  filter(year == 2006) %>% 
  summarize(avg_deaths_06 = mean(total_deaths))

kable(list(asia_96,asia_01,asia_06), caption = "Asia Deaths 1996-2006")

Asia Deaths 1996-2006

country avg_deaths_96 Pakistan 155.42988 Sri Lanka 85.28997

country avg_deaths_01 Pakistan 151.25352 Sri Lanka 72.16239

country avg_deaths_06 Pakistan 146.09296 Sri Lanka 66.04455

#Asia 2007-2017
asia_07 <- na.omit(asia)  %>% 
  group_by(country) %>% 
  filter(year == 2007) %>% 
  summarize(avg_deaths_07 = mean(total_deaths))

asia_12 <- na.omit(asia)  %>% 
  group_by(country) %>% 
  filter(year == 2012) %>% 
  summarize(avg_deaths_12 = mean(total_deaths))

asia_17 <- na.omit(asia)  %>% 
  group_by(country) %>% 
  filter(year == 2017) %>% 
  summarize(avg_deaths_17 = mean(total_deaths))

kable(list(asia_07,asia_12,asia_17), caption = "Asia Deaths 2007-2017")

Asia Deaths 2007-2017

country avg_deaths_07 Pakistan 143.81724 Sri Lanka 66.05987

country avg_deaths_12 Pakistan 133.93887 Sri Lanka 59.22433

country avg_deaths_17 Pakistan 123.21548 Sri Lanka 38.46264

#Oceania 1996-2006
oceania_96 <- na.omit(oceania)  %>% 
  group_by(country) %>% 
  filter(year == 1996) %>% 
  summarize(avg_deaths_96 = mean(total_deaths))

oceania_01 <- na.omit(oceania)  %>% 
  group_by(country) %>% 
  filter(year == 2001) %>% 
  summarize(avg_deaths_01 = mean(total_deaths))

oceania_06 <- na.omit(oceania)  %>% 
  group_by(country) %>% 
  filter(year == 2006) %>% 
  summarize(avg_deaths_06 = mean(total_deaths))

kable(list(oceania_96,oceania_01,oceania_06), caption = "Oceania Deaths 1996-2006")

Oceania Deaths 1996-2006

country avg_deaths_96 Australia 23.04465 New Zealand 21.15988

country avg_deaths_01 Australia 18.58572 New Zealand 16.91014

country avg_deaths_06 Australia 14.92239 New Zealand 13.76706

#Oceania 2007-2017
oceania_07 <- na.omit(oceania)  %>% 
  group_by(country) %>% 
  filter(year == 2007) %>% 
  summarize(avg_deaths_07 = mean(total_deaths))

oceania_12 <- na.omit(oceania)  %>% 
  group_by(country) %>% 
  filter(year == 2012) %>% 
  summarize(avg_deaths_12 = mean(total_deaths))

oceania_17 <- na.omit(oceania)  %>% 
  group_by(country) %>% 
  filter(year == 2017) %>% 
  summarize(avg_deaths_17 = mean(total_deaths))

kable(list(oceania_07,oceania_12,oceania_17), caption = "Oceania Deaths 2007-2017")

Oceania Deaths 2007-2017

country avg_deaths_07 Australia 14.92140 New Zealand 13.58658

country avg_deaths_12 Australia 12.65973 New Zealand 10.91224

country avg_deaths_17 Australia 10.795952 New Zealand 8.598757

---

Let’s graph the previous tables!

The first decade 1996-2006.

---

This shows the second decade 2007-2017.

---

By comparing each pollutant type, we can determine which year and country had the highest numbers of deaths

Indoor Deaths

---

Outdoor Deaths

---

Ozone Deaths

Which is worse?

outdoor or indoor pollution?

Let’s reintroduce a graph we looked at earlier. Instead this time we will combine the pollutant types together.

We cannot conclude which is worse.

• High Populated Countries:
  • Outdoor pollution seems to be more detrimental with the exception of two countries in this sample set.
• Low Populated Countries:
  • it’s half-and-half

Summary

• Which country has the highest average death count?
  • High Population: Pakistan
  • Low Population: Malawi
• Has the percentage of the affected population decreased or increased over time?
  • Generally it is decreasing for both High and Low populated countries
• Which pollutant type has the greatest number of deaths?
  • High Population: Indoor Pollution
  • Low Population: Indoor Pollution
• How has the death count changed over the past two decades?
  • 1996-2006:
    • High Population: Decreases
    • Low Population: Decreases
  • 2007-2017:
    • High Population: Decreases
    • Low Population: Decreases
• Which year and country had the highest number of deaths per pollutant type?
  • We looked at years 1996-2017
  • Indoor: Pakistan and Malawi were mainly affected in 1996
  • Outdoor: Serbia and Pakistan were the top countries.
    • 2011 was the worst for Pakistan.
    • 1997 was the worst for Serbia
    • Sri Lanka and Tonga increased, but Sri Lanka had a steep decrease after 2015
  • Outdoor Ozone: Pakistan and Malawi were the top countries.
    • 1997 was the worst for Pakistan.
    • 1998 was the worst for Malawi
    • United States was the second highest amount of deaths among the higher populated countries
    • Pakistan decreased and then slightly increased
• Which is worse - outdoor or indoor pollution?
  • Inconclusive

Sources

• [https://www.kaggle.com/datasets/akshat0giri/death-due-to-air-pollution-19902017 ]

• [https://www.worldometers.info/population/world/]

• [https://www.who.int/health-topics/air-pollution#tab=tab_1]

• [https://www.epa.gov/ground-level-ozone-pollution/ground-level-ozone-basics]

• [https://www.health.nsw.gov.au/environment/air/Pages/outdoor-air-pollution.aspx]

• [https://www.mana.md/indoor-air-vs-outdoor-air/]

• [https://www.kaggle.com/datasets/imdevskp/world-population-19602018]